Description
This comprehensive guide dives into the essential methodologies of statistical disclosure control specifically tailored for microdata. It addresses the critical need for data confidentiality in the era of open data and emphasizes the importance of maintaining privacy while still allowing for robust data analysis. With practical examples and applications utilizing R programming, the author lays out a structured approach for researchers and statisticians alike, making complex statistical techniques accessible.
Readers will appreciate the meticulous explanations of various disclosure control methods, which not only enhance their understanding of protecting sensitive information but also empower them to apply these strategies in real-world datasets. Through a combination of theoretical insights and hands-on applications, the book serves as a crucial resource for anyone looking to navigate the delicate balance between data utility and privacy, ultimately contributing to the responsible use of microdata in research and analysis.
Readers will appreciate the meticulous explanations of various disclosure control methods, which not only enhance their understanding of protecting sensitive information but also empower them to apply these strategies in real-world datasets. Through a combination of theoretical insights and hands-on applications, the book serves as a crucial resource for anyone looking to navigate the delicate balance between data utility and privacy, ultimately contributing to the responsible use of microdata in research and analysis.
Book Details
Format
Paperback
Pages
306 pages
Language
English
Published
Jul 28, 2018
Publisher
Springer
Edition
Softcover reprint of the original 1st ed. 2017
Editions
4 editions
ISBN-10
3319843621
ISBN-13
9783319843629